The Gixel Array Descriptor (GAD) for Multi-Modal Image Matching

Figure 10. More results for matching with GAD (left) and recall vs. 1 − precision curve comparison (right). (a) Matching with JPEG
compression (image 1 and 2 from the “Ubc” series [1]) (b) Matching with illumination change (image 1 and 4 from the “Leuven” series [1])
likely helps in reducing the impact of JPEG compression.
In Fig.10(b) with illumination change, the GAD has a recall
rate slightly inferior to other descriptors, but still finds
a large number of correct matches with almost no errors.
5.5. Processing Time
GAD’s computation process is time-consuming compared
to state-of-the-art descriptors, but no efforts at optimization
have been made yet. For examples, Fig.1 (size
512x512) takes GAD 8.9 seconds, while SURF needs 0.7s;
Fig.10(b) (size 900x600) takes GAD 19.5s, while SURF
needs 1.3s. At this point, speed is not a primary concern in
our research, but we’ll pursue optimizations in future work.
6. Conclusion
We introduce a novel descriptor unit called a Gixel,
which uses an additive scoring method to extract surrounding
edge information. We show that a circular array of Gixels
will sample edge information in overlapping regions to
make the descriptor more discriminative and it can be invariant
to rotation and scale. Experiments demonstrate the
superiority of the Gixel array descriptor (GAD) for multimodal
matching, while maintaining a performance comparable
to state-of-the-art descriptors on traditional single
modality matching.
The GAD still has some limitations in its current development
status. We have put little effort into optimization,
so the run time is slow. In addition, though GAD exhibits
rotation and scale invariance, large viewpoint changes may
reduce performance, and we have not addressed that issue
yet. Finally, as a feature built sole on edges, GAD may not
perform well in situations where edges are rare. These issues
will be investigated sin our future work.
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